Publications [#363138] of Jian-Guo Liu

Papers Published

1. Liu, JG; Wang, Z; Zhang, Y; Zhou, Z, RIGOROUS JUSTIFICATION OF THE FOKKER-PLANCK EQUATIONS OF NEURAL NETWORKS BASED ON AN ITERATION PERSPECTIVE, SIAM Journal on Mathematical Analysis, vol. 54 no. 1 (January, 2022), pp. 1270-1312, Society for Industrial & Applied Mathematics (SIAM) [doi] [high impact paper]
   (last updated on 2025/02/21)

   Abstract:
   In this work, the primary goal is to establish a rigorous connection between the Fokker-Planck equation of neural networks and its microscopic model: the diffusion-jump stochastic process that captures the mean-field behavior of collections of neurons in the integrate-and-fire model. The proof is based on a novel iteration scheme: with an auxiliary random variable counting the firing events, both the density function of the stochastic process and the solution of the PDE problem admit series representations, and thus the difficulty in verifying the link between the density function and the PDE solution in each subproblem is greatly mitigated. The iteration approach provides a generic framework for integrating the probability approach with PDE techniques, with which we prove that the density function of the diffusion-jump stochastic process is indeed the classical solution of the Fokker-Planck equation with a unique flux-shift structure.